FYPs/Thesis/Journal from Higher Education Institutions in Hong Kong


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Date: From


Institution Title Type Date Author(s) Abstract Link
HKUST Construction Planning of Prefabricated Units Leveraging BIM and Resource Leveling Techniques FYP 06/2018 WONG, Kok Yiu
YEUNG, Ching Hei
As a compact city with limited amount of available land and vast population, Hong Kong is currently facing the massive demand for housing. This phenomenon has been driving the construction industry to enhance the productivity of construction projects, particularly for residential buildings. In recent years, the Hong Kong government has been investigating the feasibility of Modular Integrated Construction (MIC). MIC refers to a construction method where volumetric modules are prefabricated in factories and then assembled at a construction site to form a building. The productivity of this method has been demonstrated by numerous projects in foreign countries, such as Singapore and China. In view of the proven benefits of MIC, the Hong Kong government has proposed three pilot projects recently, which will adopt MIC to construct resident buildings. However, the local industry possesses insufficient experience in managing this kind of construction projects. This report presents an optimization framework, which integrates Genetic Algorithm (GA) and Building Information Modeling (BIM) to perform resource leveling based on constraints of a MIC project. An illustrative case demonstrates the functionalities of GA and BIM in optimizing the schedule of a MIC project. The proposed framework aims to provide the industry practitioners with a general guideline for scheduling a MIC project. N.A.
HKUST Quantification of construction and demolition waste prevented by BIM-based design validation: Case studies in South Korea Journal 01/2016 Won, J., Cheng, J.C.P., and Lee, G. Waste generated in construction and demolition processes comprised around 50% of the solid waste in South Korea in 2013. Many cases show that design validation based on building information modeling (BIM) is an effective means to reduce the amount of construction waste since construction waste is mainly generated due to improper design and unexpected changes in the design and construction phases. However, the amount of construction waste that could be avoided by adopting BIM-based design validation has been unknown. This paper aims to estimate the amount of construction waste prevented by a BIM-based design validation process based on the amount of construction waste that might be generated due to design errors. Two project cases in South Korea were studied in this paper, with 381 and 136 design errors detected, respectively during the BIM-based design validation. Each design error was categorized according to its cause and the likelihood of detection before construction. The case studies show that BIM-based design validation could prevent 4.3–15.2% of construction waste that might have been generated without using BIM. Link
HKUST Identifying potential opportunities of building information modeling for construction and demolition waste management and minimization Journal 03/2017 Won, J., and Cheng, J.C.P. The amount of waste generated in construction and demolition (C&D) processes is enormous. Therefore, many studies on efficient C&D waste minimization and management have been conducted. However, 21 process-related and 8 technology-related limitations in C&D waste management and minimization have not yet been resolved. Building information modeling (BIM) helps project participants improve the processes and technologies in the planning, design, construction, and demolition phases, thereby managing and minimizing C&D waste efficiently. Therefore, this paper identifies the potential opportunities of BIM for efficient C&D waste management and minimization, such as design review, 3D coordination, quantity take-off, phase planning, site utilization planning, construction system design, digital fabrication, and 3D control and planning. The BIM-based approaches can support C&D waste management and minimization processes and technologies by addressing existing limitations through in-depth literature review. The roles of project participants and information required for each BIM-based approach in C&D waste management and minimization are discussed with illustrative process maps. Link
HKUST Integration of Building Information Modeling and Internet of Things for Facility Maintenance Management Thesis 03/2019 Weiwei CHEN Facility management (FM) accounts for more than two thirds of the total cost of the whole life cycle of a building. FM staff do have inadequate visualization and often have difficulty in querying information using 2D drawings and traditional facility management systems. Currently, building information modeling (BIM) is increasingly applied to FM in the operations and maintenance (O&M) stage. BIM represents the geometric and semantic information of building facilities in 3D object-based digital models and enables facility managers to manage building facilities better in the O&M stage. At the same time, the Internet of Things (IoT) technology can be used to acquire operational data of building facilities and real-time environmental data to support FM. However, few studies have used BIM and IoT technologies together for automated management and maintenance of building facilities. Around 65%~80% of the FM comes from facility maintenance management (FMM). However, there is a lack of efficient maintenance strategies and appropriate decision making approaches that can reduce FMM costs. Facility managers usually undertake reactive maintenance or preventive maintenance strategies in the O&M stage. However, reactive maintenance cannot prevent failures and preventive maintenance cannot predict the future condition of building components, which leads to maintenance actions being performed after failure has occurred and it cannot keep the functionality of a building consistent. This study aims to apply a predictive maintenance strategy with BIM and IoT technologies to overcome these limitations. In addition, there is an information interoperability problem among BIM, IoT and the FM system. Therefore, this study aims to leverage the BIM and IoT technologies to improve the efficiency of FMM and to address the information interoperability problem of integrating BIM, IoT and the FM system.

In order to improve the efficiency of FMM, an FMM framework is proposed based on BIM and facility management systems (FMSs), which can provide automatic scheduling of maintenance work orders (MWOs) to enhance good decision making in FMM. In this framework, data are mapped between BIM and FMSs according to the developed Industry Foundation Classes (IFC) extension of maintenance tasks and MWO information in order to achieve data integration. Geometric and semantic information of the failure components is extracted from the BIM models in order to calculate the optimal maintenance path in the BIM environment. Moreover, the MWO schedule is automatically generated using a modified Dijkstra algorithm that considers four factors, namely, problem type, emergency level, distance among components, and location.

In order to provide a better maintenance strategy for building facilities, a data-driven predictive maintenance framework based on BIM and IoT technologies for FMM has been developed. The framework consists of an information layer and an application layer. Data collection and data integration among the BIM models, FM system, and IoT system are undertaken in the information layer, while the application layer contains four modules to achieve predictive maintenance, namely: (1) condition monitoring and sensor data acquisition, (2) condition assessment module, (3) condition prediction module, and (4) maintenance planning module. In addition, machine learning algorithms, i.e. artificial neural network (ANN) and support vector machine (SVM), are used to predict the future condition of building components.

For the information interoperability problem among BIM, IoT and FM system, an ontology-based methodology framework is proposed for data integration among the BIM, IoT and FM domains. The ontology-based approach is developed as a tool to facilitate knowledge management in BIM- and IoT-based FMM and improve the data integration process. First, three ontologies are developed for BIM, IoT, and FMM respectively according to the ontology development process and facility information requirement. Second, an ontology mapping method is designed to integrate the three developed ontologies based on mapping rules. Moreover, ontology reasoning rules are developed based on description logics to infer implicit facts from the integrated ontology and support quick information querying on FMM. The developed framework is validated through an illustrative example.

This research provides an automatic work order scheduling approach in FMM and predictive maintenance strategy for building facilities, thereby enabling great saving in time and labor costs for facility staff. In addition, the proposed ontology-based methodology can address the information interoperability problem and integrate data from BIM, IoT and FM system for facility maintenance activities. In the future, the ontology-based methodology will be applied for the operation management of building facilities.
HKUST Automatic as-built BIM creation of precast concrete bridge deck panels using laser scan data Journal 02/2018 Wang, Q., Sohn, H., and Cheng, J.C.P. Precast concrete bridge deck panels are commonly used for bridge constructions because they enable faster construction and have less impact on traffic flow. The quality of connections between adjacent precast elements must be ensured to guarantee the overall structural integrity of precast systems. Therefore, the dimensional quality of precast concrete panels should be inspected before they are shipped to construction sites for installation. However, current quality inspection of precast concrete elements primarily relies on manual inspection. Furthermore, the as-built dimensions of precast elements are usually stored in paper sheets or Microsoft Excel spreadsheets, making it difficult to visualize and manage the as-built dimensions. This study develops a technique to automatically estimate the dimensions of precast concrete bridge deck panels and create as-built building information modeling (BIM) models to store the real dimensions of the panels. First, the proposed technique conducts scan planning to find the optimal scanner locations for scan data acquisition. Then, the scan data of the target panel are acquired and preprocessed to remove noise data and to register multiple scans in a global coordinate system. From the registered scan data, the as-built geometries of the target panel are estimated. In the last step, an as-built BIM model is created on the basis of the previously estimated geometries. The proposed technique is validated on a laboratory-scale specimen and a full-scale precast concrete bridge deck panel. The experimental results show that the proposed technique can accurately and efficiently estimate the dimensions of full-scale precast concrete bridge deck panels with an accuracy of 3 mm and automatically create as-built BIM models of the panels. Link
HKUST Automated quality assessment of precast concrete elements with geometry irregularities using terrestrial laser scanning Journal 04/2016 Wang, Q., Kim, M.-K., Cheng, J.C.P., and Sohn, H. Precast concrete elements are popularly used and it is important to ensure that the dimensions of individual elements conforms to design codes. However, the current quality assessment of precast concrete elements is inaccurate and time-consuming. To address the problems, this study presents an automated quality assessment technique which estimates the dimensions of precast concrete elements with geometry irregularities using terrestrial laser scanners (TLS). While the scan data obtained from TLS represent the as-built condition of an element, a Building Information Modeling (BIM) model stores the as-design condition of the element. Taking the BIM model as a reference, the scan data are processed to estimate the as-built dimensions of the element. Experiments on a specimen demonstrated that the proposed technique can estimate the dimensions of elements effectively and accurately. Furthermore, a mirror-aided scanning approach, which aims to achieve reduced incident angles in real scanning environments, is proposed and validated by experiments. Link